POLS 2972Q

Quantitative Analysis in Political Science

Lecture 8 | Data Visualization II

Plan for Today

Continue discussing plots in ggplot

Examine Bar graphs

Examine editing labels and themes

In class activity

Plots in R

The Anatomy of ggplot

Standard:

ggplot(data = <DATA>) + 
        <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

Recall (again…)
- ggplot() is the plot function
  - Creates a coordinate system that you can add layers to
- (Data =) is where you specify the dataset that you are using
- geom_point() is an added layer, which specifies how the data will be plotted
- mapping defines how variables in your dataset are mapped to visual properties
- aes specify which variables to map to the x and y axes

Using pipes:

<DATA> %>%
        ggplot() + 
        <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

Plotting Functions

ggplot Cheatsheet

Aesthetics

Color / Fill / Alpha (transparency)

Size

Shape

Points / Lines / Text

Faceting

Faceting creates subplots that each display one subset of the data.
- useful for categorical variables
For a single variable, use facet_wrap(~ <VARIABLE NAME>)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class)) + 
  facet_wrap(~ class, nrow = 2)

For a combination of two variables, use `facet_grid( ~ )

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class)) + 
  facet_grid(drv ~ cyl)

Geometric Objects

Geoms
- A geom is the geometrical object that a plot uses to represent data
- People often describe plots by the type of geom that the plot uses
- Every geom function in ggplot takes a mapping argument
  - Not every aesthetic works with every geom
    - You could set the shape of a point, but you couldn’t set the “shape” of a line

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

Multiple Geoms

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

What do you notice about the code above?

How could this code be more efficient?

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

What is the difference between these two code chunks?
- Global vs local mapping
- By passing a set of mappings to ggplot(), this will treat the mapping as global and apply to EACH geom
- By passing a set of mappings to geom, this will treat the mapping as local and apply to ONLY that geom

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth()

Other `ggplot` functions

coord_flip flips the x and y axis to improve the readability of plots

scales change the formatting of x and y axes

plotly makes plots interactive; you can hover over points/lines for more information

labs allows you to add/edit a title, subtitle, a caption, and change the x and y axis labels

gganimate allows you to animate plots into gifs

Barplot

Bar plots show the relationship between a numeric and categorical variable

Bar charts, histograms, and frequency polygons bin your data and then plot bin counts

library(tidyverse)

diamonds

# A tibble: 53,940 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
 7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
 8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
 9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
# ℹ 53,930 more rows

?diamonds

Let’s build a bar graph that examines the different types of “cut” in the data.

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut))

What do we notice about this plot?
- Where did “count” come from?

`stat_count()`

Many graphs, like scatterplots, plot the raw values of your dataset. Other graphs, like bar charts, calculate new values to plot. How does ggplot() compute this?

geom_bar() utilizes stat_count() as the default way to make statistical transformations for bar graphs

?geom_bar

You can generally use geoms and stats interchangeably.

Every geom has a default stat

Every stat has a default geom

This means that you can typically use geoms without worrying about the underlying statistical transformation

ggplot(data = diamonds) + 
  stat_count(mapping = aes(x = cut))

Most of the time, we are not going to be concerned with changing stat
stat = "identity"

demo <- tribble(
  ~cut,         ~freq,
  "Fair",       1610,
  "Good",       4906,
  "Very Good",  12082,
  "Premium",    13791,
  "Ideal",      21551
)

ggplot(data = demo) +
  geom_bar(mapping = aes(x = cut, y = freq), stat = "identity")

y = stat(prop), group = 1

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, y = stat(prop), group = 1))

Color and Position Adjustments

Color
- You can use color =
- You can use fill =
Which one is more useful?

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, color = cut))

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = cut))

How does the color aesthetic change if we use the variable “clarity”

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity))

Position
- The stacking from the plot above is performed automatically when position = is left unchanged
- If you don’t want a stacked bar chart, you can use one of three other options
  - “identity”
  - “dodge”
  - “fill”

Identity
- Place each object exactly where it falls in the context of the graph
- Overlaps the bars
- Not useful for bar graphs

ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar(position = "identity")

ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar(alpha = 1/5, position = "identity")

Fill
- Like stacking, but makes each set of stacked bars the same height
- Easier to compare proportions across groups

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "fill")

Dodge
- Places overlapping objects directly beside one another
- Easier to compare individual values

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")

Graphics for Communication

When exploring data, yourself, you are aware of the data/graph that you have created

When creating graphs for explanation, you need to ensure that your audience understands the graph

Labels

Title
x - axis
y - axis

#adding a title
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") + 
  labs(title = "Cut of Diamond by Clarity")

#adding a x-axis
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") + 
  labs(title = "Cut of Diamond by Clarity",
       x = "Diamond Cut")

#adding a y-axis
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") + 
  labs(title = "Cut of Diamond by Clarity",
       x = "Diamond Cut",
       y = "Number of Diamonds")

Scales

Adjusting the scales allows you to control the range of the x and y-axis
`ggplot() automatically adds default scales behind the scenes

#change y-axis scale
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") + 
  labs(title = "Cut of Diamond by Clarity",
       x = "Diamond Cut",
       y = "Number of Diamonds") +
  scale_y_continuous(breaks = seq(0, 6000, by = 500), limits = c(0, 6000))

#change x-axis scale (labels)
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") + 
  labs(title = "Cut of Diamond by Clarity",
       x = "Diamond Cut",
       y = "Number of Diamonds") +
  scale_y_continuous(breaks = seq(0, 6000, by = 500), limits = c(0, 6000)) +
  scale_x_discrete(labels = c("eh", "ok", "better", "wow", "hey now"))

Themes

Default theme has a grey background
Not the most visually pleasing

#default theme
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge")

#theme_bw()
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") +
  theme_bw()

#theme_bw() with grid removed
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") +
  scale_y_continuous(breaks = seq(0, 6000, by = 500), limits = c(0, 6000)) +
  theme_bw() +
  theme(axis.line = element_line(color='black'),
    plot.background = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank())

#theme_bw() with origin at 0,0
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity), position = "dodge") +
  scale_y_continuous(breaks = seq(0, 6000, by = 500), limits = c(0, 6000), expand = c(0,0)) +
  theme_bw() +
  theme(axis.line = element_line(color='black'),
    plot.background = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank())

In-Class Activity

We are going to practice producing and altering ggplot()
Get with a partner and work through Week 3 | Class Activity

For Next Class

Review what you learned today
Do new set of readings:
- We are going to discuss how we transform data in R with Tidyverse
- R4DS Chapter 3
  - Follow along the exercises with your own computer
Bring your laptops to class

POLS 2972Q

Plan for Today

Plots in R

The Anatomy of ggplot

Plotting Functions

Aesthetics

Faceting

Geometric Objects

Multiple Geoms

Other ggplot functions

Barplot

stat_count()

Color and Position Adjustments

Graphics for Communication

Labels

Scales

Themes

In-Class Activity

For Next Class

Other `ggplot` functions

`stat_count()`